Gene expression programming algorithm for transient security classification

  • Authors:

  • Almoataz Y. Abdelaziz;S. F. Mekhamer;H. M. Khattab;M. L. A. Badr;Bijaya Ketan Panigrahi

  • Affiliations:

  • Department of Electrical Power & Machines, Faculty of Engineering, Ain Shams University, Cairo, Egypt;Department of Electrical Power & Machines, Faculty of Engineering, Ain Shams University, Cairo, Egypt;Engineering for the Petroleum and Process Industries, (ENPPI), Cairo, Egypt;Department of Electrical Power & Machines, Faculty of Engineering, Ain Shams University, Cairo, Egypt;Department of Electrical Engineering, Indian Institute of Technology, Delhi, India

  • Venue:
  • SEMCCO'12 Proceedings of the Third international conference on Swarm, Evolutionary, and Memetic Computing
  • Year:
  • 2012

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Abstract

In this paper, a gene expression programming (GEP) based algorithm is implemented for power system transient security classification. The GEP algorithms as evolutionary algorithms for pattern classification have recently received attention for classification problems because they can perform global searches. The proposed methodology applies the GEP for the first time in transient security assessment and classification problems of power systems. The proposed algorithm is examined using different IEEE standard test systems. Power system three phase short circuit contingency has been used to test the proposed algorithm. The algorithm checks the static security status of the power system then classifies the transient security of the power system as secure or not secure. Performance of the algorithm is compared with other neural network based classification algorithms to show its superiority for transient security classification.